這是我第一次在 pandas 中使用多重索引，我需要一些幫助來合并兩個具有分層列的數據幀。這是我的兩個數據框：col_index = pd.MultiIndex.from_product([['a', 'b', 'c'], ['w', 'x']])df1 = pd.DataFrame(np.ones([4,6]),columns=col_index, index=range(4)) a b c w x w x w x0 1.0 1.0 1.0 1.0 1.0 1.01 1.0 1.0 1.0 1.0 1.0 1.02 1.0 1.0 1.0 1.0 1.0 1.03 1.0 1.0 1.0 1.0 1.0 1.0df2 = pd.DataFrame(np.zeros([2,6]),columns=col_index, index=range(2)) a b c w x w x w x0 0.0 0.0 0.0 0.0 0.0 0.01 0.0 0.0 0.0 0.0 0.0 0.0當我使用合并方法時，我得到以下結果：pd.merge(df1,df2, how='left', suffixes=('', '_2'), left_index = True, right_index= True )) a b c a_2 b_2 c_2 w x w x w x w x w x w x0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.01 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.02 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN NaN NaN NaN3 1.0 1.0 1.0 1.0 1.0 1.0 NaN NaN NaN NaN NaN NaN但我想在較低級別上合并兩個數據幀，后綴對 ['w', 'x'] 生效，如下所示： a b c w w_2 x x_2 w w_2 x x_2 w w_2 x x_20 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.01 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.02 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
1 回答
忽然笑
TA貢獻1806條經驗 獲得超5個贊
您可以將joinormerge與swaplevel()or 一起使用reorder_levels。然后使用.sort_index()和 傳遞axis=1來按索引列排序。
.join()當您像這樣對索引進行合并時會更好。.swaplevel()當有兩個級別時更好（如本例），而當.reorder_levels()有 3 個或更多級別時更好。
以下是這些方法的 4 種組合。對于這個具體的例子，我認為.join()/.swaplevel()是最簡單的（參見最后一個例子）：
df3 = (df1.reorder_levels([1,0],axis=1)
.join(df2.reorder_levels([1,0],axis=1), rsuffix='_2')
.reorder_levels([1,0],axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[1]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (pd.merge(df1.reorder_levels([1,0],axis=1),
df2.reorder_levels([1,0],axis=1),
how='left', left_index=True, right_index=True, suffixes = ('', '_2'))
.reorder_levels([1,0],axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[2]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (pd.merge(df1.swaplevel(axis=1),
df2.swaplevel(axis=1),
how='left', left_index=True, right_index=True, suffixes = ('', '_2'))
.swaplevel(axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[3]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
df3 = (df1.swaplevel(i=0,j=1, axis=1)
.join(df2.swaplevel(axis=1), rsuffix='_2')
.swaplevel(axis=1).sort_index(axis=1, level=[0, 1]))
df3
Out[4]:
a b c
w w_2 x x_2 w w_2 x x_2 w w_2 x x_2
0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0
2 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
3 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN 1.0 NaN
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